Module 5: Sequence models: RNNs and LSTMs

AINS6003 — Deep Learning & Neural Networks

90-minute lecture deck

Essential question: How do recurrent architectures model temporal structure and long-range dependencies?

Why This Matters

An operations group wants to forecast event sequences where order matters and recent context may not be sufficient

For non-CS graduate students, the goal is not to become a software engineer in one class session. The goal is to learn how to read an AI workflow, ask better questions, and explain what the evidence does or does not support.

90-Minute Teaching Arc

Time	Segment	Purpose
0-10	Orientation and stakes	Connect the topic to a professional decision.
10-25	Conceptual model	Build intuition before code or formulas.
25-40	Worked example	Translate vocabulary into a small concrete case.
40-55	Evidence and interpretation	Read outputs, metrics, or artifacts carefully.
55-70	Guided student activity	Let students change one variable and observe.
70-82	Risk, limits, and communication	Name what could go wrong and how to explain it.
82-90	Assignment handoff	Clarify deliverable, rubric, and next step.

Learning Outcomes

• explain the core technical idea in precise neural-network vocabulary

• connect architecture and training choices to the shape of the data and task

• run or interpret the module lab as reproducible evidence

• identify limitations, failure modes, and next steps for a defensible experiment

By the end, students should be able to explain the idea without hiding behind jargon and should know what evidence would make a recommendation stronger.

Plain-Language Framing

Ask students to complete this sentence before the technical terms arrive:

This method helps a professional decide whether ______ because it uses ______ as evidence.

Then revisit the sentence at the end of the lecture and improve it with module vocabulary.

Core Vocabulary

• sequence-to-one and sequence-to-sequence framing: define it in one sentence, then connect it to the scenario.

• hidden state and recurrence: define it in one sentence, then connect it to the scenario.

• vanishing gradients in long contexts: define it in one sentence, then connect it to the scenario.

• gates in LSTM and GRU cells: define it in one sentence, then connect it to the scenario.

• teacher forcing, masking, and padding: define it in one sentence, then connect it to the scenario.

Instructor note: pause after each term and ask for a student-generated example.

Conceptual Model

Use a three-part model:

1. Input: What information is available?

2. Transformation: What does the AI or analytic method do to the information?

3. Decision: What human or organizational action could change because of the result?

This keeps the discussion accessible for students new to Python.

Worked Example Setup

Use the professional scenario as the example case. Ask:

• Who owns the decision?

• What evidence would they trust?

• What would count as a bad recommendation?

• What would a cautious first experiment look like?

Write the answers on the board before opening the lab.

Method Pattern

• Define the alignment between inputs, timesteps, targets, and prediction horizon.

• Use padding and masks explicitly when sequences have different lengths.

• Compare recurrent models with simpler baselines such as lag features or temporal convolutions.

• Diagnose whether performance comes from sequence order or from static shortcuts.

This is the repeatable professional move students should practice across the program.

Lab Bridge

Lab notebook: Module 5 Lab: Sequence prediction

Use Colab as the recommended first environment. Have students run all cells first, then change exactly one value, threshold, feature, or assumption. The point is observation and interpretation, not typing a lot of code from scratch.

Reading Lab Outputs

When students see a number, plot, table, or printed result, ask four questions:

1. What changed?

2. Is the change large enough to matter?

3. What assumption produced the result?

4. What would we need before using this outside the toy setting?

Guided Activity

Students work in pairs or small groups:

• Run the lab unchanged.

• Change one small input or parameter.

• Capture the before/after result.

• Write a two-sentence interpretation for a nontechnical stakeholder.

Share two examples with the room.

Common Failure Modes

• Leaking future information through preprocessing windows.

• Ignoring variable-length sequences and padding artifacts.

• Assuming an LSTM solves all long-range dependency problems.

• Evaluating only aggregate error when rare sequence patterns drive risk.

A strong lecture names these risks before students over-trust the output.

Discussion Checkpoint

Use these prompts at the 60-minute mark:

• What did the method make easier to see?

• What did the method hide or simplify?

• Who might be harmed by a confident but wrong interpretation?

• What evidence would make you more comfortable recommending action?

Assignment Handoff

Module 5 Assignment: Sequence modeling design note

• Define a sequence prediction task and identify input/output alignment

• Compare simple RNN, LSTM, GRU, and one-dimensional convolution choices

• Run the starter LSTM on synthetic ordered data and inspect tensor shapes

• Explain one long-range dependency risk and a mitigation strategy

Students should leave knowing the artifact they are producing, the evidence they must include, and the limitation they must state.

Rubric Translation

Translate grading into student language:

• Correct: terms and results are used accurately.

• Evidence-based: claims point to notebook output, scenario facts, or documented assumptions.

• Context-aware: the recommendation fits the stakeholder decision.

• Honest: limitations and risks are named clearly.

Closing Reflection

Exit prompt:

In one paragraph, explain what this module helps you decide, what evidence the lab produced, and what you would still need before trusting the result in a real organization.

Collect this verbally, in the LMS, or as the opening paragraph of the assignment.

Instructor Timing Notes

If time runs short, preserve the lab bridge and assignment handoff. Compress vocabulary rather than skipping interpretation. Students new to AI need repeated practice moving from output to meaning.

If time runs long, add a second student share-out focused on limitations and stakeholder communication.

If Students Are New to Python

• Explain that a notebook mixes text, code, and output in one page.

• Run the notebook once before asking students to change anything.

• Ask for one small change, not open-ended coding.

• Grade interpretation, evidence, and limitation statements more than syntax fluency.

• Keep Colab as the first-choice environment unless the activity truly needs the full repository.

• Give students permission to describe the result first, then refine the vocabulary after discussion.